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Multi-users interaction anomalous subgraph detection for event mining
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AI
DOI：10.1016/j.neucom.2022.08.072.png)
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Abstract
En 中文
Event detection on networks is an important research task in data mining. Most previous approaches usu-ally detect a particular type of event that satisfies the predefined rules in the model, i.e., data-driven methods. However, few methods consider the diverse interests of multiple experts during the detection process to discover unexplored events. In this paper, we regard interactive event detection on attributed networks as Multi-users Interaction Anomalous Subgraph Detection (MIASD), where events are repre-sented as connected subgraphs on networks. The core of our method is automatically maximizing a non-parametric scan statistic over connected subgraphs to identify the most anomalous subgraph as the event and integrating the interactions from multiple experts simultaneously, i.e., under both data -driven and user-driven paradigm. In our approach, we first define individual and shared environments of experts on attributed networks to model the interests of experts, including the interactive operations and Recommended Interaction Domain. Afterwards, we propose an efficient anomalous subgraph detec-tion algorithm in which each expert gives feedback separately on the vertices in the Recommended Interaction Domain; then, the non-parametric scan statistic over the connected subgraph is approxi-mately maximized by integrating various feedback from experts into the iterative subgraph expansion process. In this way, our method retrieves the most anomalous subgraph on the network as the final event, which contains the potential unexplored information. We have conducted extensive experiments on three real-world datasets and proved that our algorithm could achieve better performance compared with several competitive baselines. Furthermore, the case study shows that our method could detect glo-bal abnormal events effectively. @2022 Elsevier B.V. All rights reserved.
Keywords:
Interactive event detection
Anomalous subgraph detection
Recommended interaction domain
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